Key interaction networks : Identifying evolutionarily conserved non-covalent interaction networks across protein families
Yehorova, Dariia ; Crean, Rory M ; Kasson, Peter M and Kamerlin, Shina C L LU
(2024)
In Protein Science
33(3).
- Abstract
Protein structure (and thus function) is dictated by non-covalent interaction networks. These can be highly evolutionarily conserved across protein families, the members of which can diverge in sequence and evolutionary history. Here we present KIN, a tool to identify and analyze conserved non-covalent interaction networks across evolutionarily related groups of proteins. KIN is available for download under a GNU General Public License, version 2, from https://www.github.com/kamerlinlab/KIN. KIN can operate on experimentally determined structures, predicted structures, or molecular dynamics trajectories, providing insight into both conserved and missing interactions across evolutionarily related proteins. This provides useful insight... (More)
Protein structure (and thus function) is dictated by non-covalent interaction networks. These can be highly evolutionarily conserved across protein families, the members of which can diverge in sequence and evolutionary history. Here we present KIN, a tool to identify and analyze conserved non-covalent interaction networks across evolutionarily related groups of proteins. KIN is available for download under a GNU General Public License, version 2, from https://www.github.com/kamerlinlab/KIN. KIN can operate on experimentally determined structures, predicted structures, or molecular dynamics trajectories, providing insight into both conserved and missing interactions across evolutionarily related proteins. This provides useful insight both into protein evolution, as well as a tool that can be exploited for protein engineering efforts. As a showcase system, we demonstrate applications of this tool to understanding the evolutionary-relevant conserved interaction networks across the class A β-lactamases.
(Less)
- author
- Yehorova, Dariia
; Crean, Rory M
; Kasson, Peter M
and Kamerlin, Shina C L
LU
- publishing date
- 2024-03
- type
- Contribution to journal
- publication status
- published
- keywords
- Algorithms, Proteins/chemistry
- in
- Protein Science
- volume
- 33
- issue
- 3
- article number
- e4911
- publisher
- The Protein Society
- external identifiers
-
- pmid:38358258
- scopus:85185346185
- ISSN
- 1469-896X
- DOI
- 10.1002/pro.4911
- language
- English
- LU publication?
- no
- additional info
- © 2024 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.
- id
- 1448ae02-ef7f-44aa-b7f3-8e88dfc27a7c
- date added to LUP
- 2025-01-11 18:20:33
- date last changed
- 2025-06-29 18:05:08
@article{1448ae02-ef7f-44aa-b7f3-8e88dfc27a7c,
abstract = {{<p>Protein structure (and thus function) is dictated by non-covalent interaction networks. These can be highly evolutionarily conserved across protein families, the members of which can diverge in sequence and evolutionary history. Here we present KIN, a tool to identify and analyze conserved non-covalent interaction networks across evolutionarily related groups of proteins. KIN is available for download under a GNU General Public License, version 2, from https://www.github.com/kamerlinlab/KIN. KIN can operate on experimentally determined structures, predicted structures, or molecular dynamics trajectories, providing insight into both conserved and missing interactions across evolutionarily related proteins. This provides useful insight both into protein evolution, as well as a tool that can be exploited for protein engineering efforts. As a showcase system, we demonstrate applications of this tool to understanding the evolutionary-relevant conserved interaction networks across the class A β-lactamases.</p>}},
author = {{Yehorova, Dariia and Crean, Rory M and Kasson, Peter M and Kamerlin, Shina C L}},
issn = {{1469-896X}},
keywords = {{Algorithms; Proteins/chemistry}},
language = {{eng}},
number = {{3}},
publisher = {{The Protein Society}},
series = {{Protein Science}},
title = {{Key interaction networks : Identifying evolutionarily conserved non-covalent interaction networks across protein families}},
url = {{http://dx.doi.org/10.1002/pro.4911}},
doi = {{10.1002/pro.4911}},
volume = {{33}},
year = {{2024}},
}